Neural Networks and Deep Learning Course Outline

Prerequisites:

• Basic programming knowledge (Python recommended)

• Familiarity with linear algebra and calculus concepts

• Understanding of machine learning fundamentals (beneficial but not required)

Course Objectives:

• Gain a foundational understanding of neural networks and deep learning.

• Implement and train various neural network architectures.

• Develop practical skills in building and applying neural networks to real-world problems.

• Establish a base for further exploration of advanced deep learning techniques and applications.

Course Duration: 4 days (flexible)

Course Outline:

Module 1: Introduction to Neural Networks

• Demystifying Neural Networks:

  • Basic structure and components

  • How they learn and process information

• Activation Functions:

  • Understanding their role in neural networks

  • Exploring different activation functions and their properties

Module 2: Building Neural Networks with Python

• Binary Classification with Logistic Regression

• Implementing a Basic Neural Network in Python using NumPy

  • Hands-on coding experience

Module 3: Exploring Shallow Neural Networks

• Architecture and Representation of Shallow Neural Networks

• Implementing Forward and Backward Propagation (vectorized form)

  • Efficient training algorithms for neural networks

Module 4: Deep Learning Architectures

• Architecture and Representation of Deep Neural Networks

• Implementing Forward and Backward Propagation in Deep Networks

  • Training complex deep learning models

Module 5 (Optional): Structuring Machine Learning Projects

• Setting up and evaluating machine learning projects effectively

• Techniques for analyzing bias, variance, and errors

• Prioritizing improvements for optimal performance